Integrated Model Predictive Control of a PV–Wind–Adiabatic CAES Hybrid Microgrid for Simultaneous Climate and Energy Optimization in a Smart Greenhouse
Main Article Content
Abstract
This study proposes an integrated energy and climate management framework for a smart greenhouse supplied by a photovoltaic–wind microgrid coupled with adiabatic compressed air energy storage. Unlike conventional storage systems operating exclusively in the electrical domain, the proposed configuration enables dual thermal reuse: heat generated during compression is stored for nocturnal heating, while expansion-induced cooling supports daytime climate regulation. A mixed-integer model predictive control strategy is developed to coordinate renewable generation, storage scheduling, grid exchange, and greenhouse temperature–humidity regulation under crop comfort constraints. The system is evaluated on a 160 m² greenhouse using reduced-order electrical and thermodynamic models suitable for predictive optimization. Simulation results demonstrate a reduction in grid electricity import of up to 34% and a 22% decrease in heating and cooling energy consumption compared with a rule-based baseline, while maintaining temperature tracking with a root mean square error below 0.9 °C. The results highlight the potential of thermally integrated compressed air storage combined with predictive multi-domain optimization to enhance renewable penetration and improve energy efficiency in controlled-environment agriculture.
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
How to Cite
References
Adeyinka, A. M., Esan, O. C., Ijaola, A. O., & Farayibi, P. K. (2024). Advancements in hybrid energy storage systems for enhancing renewable energy-to-grid integration. Sustainable Energy Research, 11, 26. https://doi.org/10.1186/s40807-024-00120-4
Alami, A.H., Orhan, M., Al Rashid, R. et al. (2022). Cooling potential for hot climates by utilizing thermal energy from compressed air energy storage. Scientific Reports, 12, 22066. https://doi.org/10.1038/s41598-022-26666-1
Chen, L., Zheng, T., Mei, S. et al. (2016). Review and prospect of compressed air energy storage system. J. Mod. Power Syst. Clean Energy 4, 529–541. https://doi.org/10.1007/s40565-016-0240-5
Chen, S., Liu, A., Tang, F., Hou, P., Lu, Y., & Yuan, P. (2025). A Review of Environmental Control Strategies and Models for Modern Agricultural Greenhouses. Sensors, 25(5), 1388. https://doi.org/10.3390/s25051388
Courtois, N., Najafiyazdi, M., Lotfalian, R., Boudreault, R., Picard, M. (2021). Multi-stage adiabatic compressed air energy storage efficiency evaluation. Applied Energy, 288, 116592. https://doi.org/10.1016/j.apenergy.2021.116592
Geweda, A.E., Saif, A.G.H., Mohamed E. Zayed, M.E. et al. (2025). Recent advances in hybrid compressed air energy storage. Alexandria Engineering Journal. 115, 12-29. https://doi.org/10.1016/j.aej.2024.11.062
Komba, N.A., Haisong, C., Liwoko, B.B. et al. (2025). A comprehensive review on compressed air energy storage. Journal of Energy Storage. 14, 115795. https://doi.org/10.1016/j.est.2025.115795
Lyu, C., Jia, Y., Xu, Z. (2020). Tube-based Model Predictive Control Approach for Real-time Operation of Energy Storage System. 2020 International Conference on Smart Grids and Energy Systems (SGES), Perth, Australia, 493-497, doi: 10.1109/SGES51519.2020.00093.
Lyu, C., Jia, Y., Xu, Z. (2021). Real-time operation optimization of microgrids with battery energy storage systems: Tube-based MPC. arXiv. https://arxiv.org/pdf/2104.04819
Madlener, R., & Latz, J. (2013). Economics of centralized and decentralized compressed air energy storage for enhanced grid integration of wind power. Applied Energy, 101, 299–309. https://doi.org/10.1016/j.apenergy.2011.09.033
Mahmood, F., Govindan, R., Bermak, A., Yang, D., Al-Ansari, T. (2023). Data-driven robust model predictive control for greenhouse temperature control. Applied Energy, 343, 121190. https://doi.org/10.1016/j.apenergy.2023.121190
Nassereddine, K., Turzynski, M., Bielokha, H., et al. (2025). Simulation of energy management system using model predictive control in AC/DC microgrid. Scientific Reports, 15, 5388. https://doi.org/10.1038/s41598-025-89036-7
Rabi, A. M., Radulovic, J., & Buick, J. M. (2023). comprehensive review of compressed air energy storage (CAES) technologies. Thermo, 3(1), 104-126. https://doi.org/10.3390/thermo3010008
Roos, P., & Haselbacher, A. (2022). Analytical modeling of advanced adiabatic compressed air energy storage: Literature review and new models. Renewable and Sustainable Energy Reviews, 163, 112464. https://doi.org/10.1016/j.rser.2022.112464
Sarmast, S., Rouindej, K., Fraser, R.A., Dusseault, M.B. (2024). Optimizing near-adiabatic compressed air energy storage. Applied Energy, 357, 122465. https://doi.org/10.1016/j.apenergy.2023.122465
Toure, I., Payman, A., Camara, M.-B., & Dakyo, B. (2024). Energy management in a renewable-based microgrid using a model predictive control method for electrical energy storage devices. Electronics, 13(23), 4651. https://doi.org/10.3390/electronics13234651
Wang, L., Zhang, Y., Xu, M., Liu, Q., Wang, B. (2024). Predictive control for greenhouse temperature and humidity and energy optimization by improved NMPC objective function algorithm. International Journal of Agricultural and Biological Engineering, 17(5), 128–136. https://doi.org/10.25165/j.ijabe.20241705.8241
Zhang, R., & Zhao, G. (2025). A comprehensive review of compressed air energy storage technologies: Current status and future trends. Journal of Renewable and Sustainable Energy, 17(2), 022702. https://doi.org/10.1063/5.0246214
Zhang, X., Gao, Z., Zhou, B. et al. (2024). Advanced compressed air energy storage systems. Engineering, 34, 246-269. https://doi.org/10.1016/j.eng.2023.12.008